Autonomic Configuration Adaptation Based on Simulation-Generated State-Transition Models
Bibliographic record
Abstract
Configuration management is a complex task, even for experienced system administrators, which makes self-managing systems a particularly desirable solution. This paper describes a novel contribution to self-managing systems, including an autonomic configuration self-optimization methodology. Our solution involves a systematic simulation method that develops a state-transition model of the behavior of a service-oriented system in terms of its configuration and performance. At run time, the system's behavior is monitored and classified in one of the model states. If this state may lead to futures that violate service level agreements, the system configuration is changed toward a safer future state. Similarly, a satisfactory state that is over-provisioned may be transitioned to a more economical satisfactory state. Aside from the typical benefits of self-optimization, our approach includes an intuitive, explainable decision model, the ability to predict the future with some accuracy avoiding trial-and-error, offline training, and the ability to improve the model at run-time. We demonstrate this methodology in an experiment where Amazon EC2 instances are added and removed to handle changing request volumes to a real service-oriented application. We show that a knowledge base generated entirely in simulation can be used to make accurate changes to a real-world application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".